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BCAFL: a secure and efficient blockchain framework for asynchronous federated learning.
1Dalian Minzu University, College of Computer Science and Engineering, Dalian, 116600, China. yunjianm@163.com.
Scientific Reports
|June 16, 2026
Summary
This study introduces BCAFL, a blockchain framework for semi-asynchronous federated learning (FL). It enhances model adaptation, reduces communication overhead, and improves security against attacks, maintaining convergence accuracy.
Area of Science:
- Artificial Intelligence
- Computer Science
- Blockchain Technology
Background:
- Traditional synchronous Federated Learning (FL) faces latency issues due to synchronization and straggler nodes.
- Asynchronous Federated Learning (AFL) improves efficiency but struggles with blockchain integration challenges like storage overhead, stale gradients, and security threats.
Purpose of the Study:
- To propose BCAFL, a decentralized blockchain framework for semi-asynchronous federated learning.
- To address storage overhead, convergence perturbations, and security threats in decentralized FL.
Main Methods:
- Utilizing InterPlanetary File System (IPFS) for off-chain storage of global model parameters.
- Integrating Model-Agnostic Meta-Learning (MAML) and PowerSGD for enhanced local adaptation and reduced communication.
- Developing a Mutual Information and Delay-Aware (MIDA) dynamic aggregation mechanism for security and stability.
- Implementing a dynamic stake-based Verifiable Random Function (VRF) committee consensus mechanism.
Main Results:
- BCAFL maintains global model convergence accuracy while reducing communication overhead compared to baseline schemes.
- The framework effectively mitigates convergence oscillations caused by asynchronous delays.
- BCAFL demonstrates defense against poisoning and Byzantine attacks.
- Consensus latency remains stable even with an expanded network scale up to 300 nodes.
Conclusions:
- BCAFL offers an effective solution for decentralized semi-asynchronous federated learning.
- The framework enhances model performance, communication efficiency, and security in heterogeneous environments.
- BCAFL provides a robust and scalable solution for blockchain-based federated learning.
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